docstr: Helper method that uses the workspace and constructs slices of it that can be used.
  
  Returns:
      An int, representing the offset of the used workspace (practically, the slice of the workspace consumed)
      A tuple of tensors representing the shared workspace.
docstr_tok: ["Helper","method","that","uses","the","workspace","and","constructs","slices","of","it","that","can","be","used",".","Returns",":","An","int",",","representing","the","offset","of","the","used","workspace","(","practically",",","the","slice","of","the","workspace","consumed",")","A","tuple","of","tensors","representing","the","shared","workspace","."]
code: def _prepare_workspace(self) -> (int, Tuple[torch.Tensor]):
      """
      Helper method that uses the workspace and constructs slices of it that can be used.
  
      Returns:
          An int, representing the offset of the used workspace (practically, the slice of the workspace consumed)
          A tuple of tensors representing the shared workspace.
      """
      used_offset, (
          denom,
          alphas,
          betas,
          llForward,
          llBackward,
      ) = super()._prepare_workspace()
  
      durations = self.tdt_workspace[: self.num_durations]
  
      return used_offset, (
          denom,
          alphas,
          betas,
          llForward,
          llBackward,
          durations,
      )
  
imports: import multiprocessing
  import random
  from typing import Optional, Tuple
  import numba
  import torch
  from numba import cuda
  from nemo.collections.asr.parts.numba.rnnt_loss.utils import global_constants, rnnt_helper
  from nemo.collections.asr.parts.numba.rnnt_loss.utils.cuda_utils import gpu_rnnt_kernel, reduce
label_desc: Use image node_id 5 for calling the GPUTDT obj's underlying member method code with example usage: obj._prepare_workspace() and returns: used_offset,  denom,  alphas,  betas,  llForward,  llBackward,  durations